The Developing Human Connectome Project: a Minimal Processing Pipeline for Neonatal Cortical Surface Reconstruction

By Antonios Makropoulos, Emma Claire Robinson, Andreas Schuh, Robert Wright, Sean P. Fitzgibbon, Jelena Bozek, Serena J. Counsell, J. Steinweg, Katy Vecchiato, Jonathan Passerat-Palmbach and 9 more. bioRxiv, 2017

527 citations recorded by the source corpus.

The Developing Human Connectome Project (dHCP) seeks to create the first 4-dimensional connectome of early life. Understanding this connectome in detail may provide insights into normal as well as abnormal patterns of brain development. Following established best practices adopted by the WUMINN Human Connectome Project and pioneered by FreeSurfer, the project utilises cortical surface-based processing pipelines. In this paper, we propose a fully automated processingmechanisms, genetic pipeline for the structural Magnetic Resonance Imaging (MRI) of the developing neonatal brain. This proposed pipeline consists of a refined framework for cortical and sub-cortical volume segmentation, cortical surface extraction and cortical surface inflation of neonatal subjects, which has been specifically designed to address considerable differences between adult and neonatal brains, as imaged using MRI. Using the proposed pipeline our results demonstrate that images collected from 465 subjects ranging from 28 to 45 weeks post-menstrual age (PMA) can be fully automatically processed; generating cortical surface models that are topologically and anatomically correct. Downstream, these surfaces will enhance comparisons of functional and diffusion MRI datasets, and support the modelling of emerging patterns of brain connectivity.

Subfields: Cognitive Neuroscience, Pediatrics, Perinatology and Child Health, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/3dbceeabc6fa523778ca602a040d23cae700ca34

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Cognitive Neuroscience

1. Matthew F. Glasser
2. Stephen M. Smith
3. Timothy S. Coalson
4. D. V. Van Essen
5. Emma Claire Robinson
6. Mark Jenkinson
7. Daniel Rueckert
8. Antonios Makropoulos

In Pediatrics, Perinatology and Child Health

1. Daniel Rueckert
2. Antonios Makropoulos
3. Mark Jenkinson
4. Stephen M. Smith

In Radiology, Nuclear Medicine and Imaging

1. Matthew F. Glasser
2. Stephen M. Smith
3. Timothy S. Coalson
4. Daniel Rueckert
5. Mark Jenkinson
6. Emma Claire Robinson
7. D. V. Van Essen
8. Antonios Makropoulos

Citations in the corpus

• Transformers in Medical Image Analysis: A Review (2022, methodology): “[121] Infant Brain dHCP [122] PSNR, SSIM MRI synthesis & Super-resolution”
• Multimodal Surface Matching with Higher-Order Smoothness Constraints☆ (2017, methodology): “Generation of surface meshes and associated shape features were carried out using dHCP Structural Pipelines (Makropoulos et al., 2017).”
• Transformers in Medical Imaging: A Survey (2022, background): “They demonstrate the superiority of PTNet both qualitatively and quantitatively compared to pix2pix [316], and pix2pixHD [317] on large-scale infant MRI dataset [318].”